The identifiability for causal effects under a type of assumptions based on conditional independence in a causal model is treated by equation method.
运用方程组求解的方法来解决一类因果效应可识别的充要条件的问题。
Absrtact: Naive Bayesian classifier is a simple and effective classifier, but its conditional independence assumption makes it unable to express the dependence among features.
摘要：朴素贝叶斯分类器是一种简单而高效的分类器，但它的条件独立性假设使其无法表示属性问的依赖关系。
If the NB conditional independence assumption actually holds, a Naive Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data.
倘若条件独立性假设确实满足，朴素贝叶斯分类器将会比判别模型，譬如逻辑回归收敛得更快，因此你只需要更少的训练数据。
Parameter estimation as minimization - intro to method of least squares, introduction to probability theory, conditional and joint probabilities, statistical independence.
以最小化问题估算参数-最小平方法介绍，机率论介绍，条件机率及联合机率，统计无关。
Naive Bayes algorithm is a simple and effective classification algorithm. However, its classification performance is affected by its conditional attribute independence assumption.
朴素贝叶斯算法是一种简单而高效的分类算法，但是它的条件独立性假设影响了其分类性能。
Naive Bayes algorithm is a simple and effective classification algorithm. However, its classification performance is affected by its conditional attribute independence assumption.
朴素贝叶斯算法是一种简单而高效的分类算法，但是它的条件独立性假设影响了其分类性能。
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